Journal
DIGITAL SIGNAL PROCESSING
Volume 90, Issue -, Pages 110-124Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.dsp.2019.04.006
Keywords
Face recognition; Dictionary learning; Subspace learning; Label relaxation model
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Funding
- National Natural Science Foundation of China [61771145, 61371148]
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Dictionary learning plays an important role in sparse representation based face recognition. Many dictionary learning algorithms have been successfully applied to face recognition. However, for corrupted data because of noise or face variations (e.g. occlusion and large pose variation), their performances decline due to the disparity between domains. In this paper, we propose a face recognition algorithm based on dictionary learning and subspace learning (DLSL). In DLSL, a new subspace learning algorithm (SL) is proposed by using sparse constraint, low-rank technology and our label relaxation model to reduce the disparity between domains. Meanwhile, we propose a high-performance dictionary learning algorithm (HPDL) by constructing the embedding term, non-local self-similarity term, and time complexity drop term. In the obtained subspace, we use HPDL to classify these mapped test samples. DLSL is compared with other 28 algorithms on FRGC, LFW, CVL, Yale B and AR face databases. Experimental results show that DLSL achieves better performance than those 28 algorithms, including many state-of-theart algorithms, such as recurrent regression neural network (RRNN), multimodal deep face recognition (MDFR) and projective low-rank representation (PLR). (C) 2019 Elsevier Inc. All rights reserved.
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